In breast cancer (BC) the employment of sequencing technologies for metagenomic analyses has allowed not only the description of the overall metagenomic landscape but also the specific microbial changes and their functional implications. Most of the available data suggest that BC is related to bacterial dysbiosis in both the gut microenvironment and breast tissue. It is hypothesized that changes in the composition and functions of several breast and gut bacterial taxa may contribute to BC development and progression through several pathways. One of the most prominent roles of gut microbiota is the regulation of steroid-hormone metabolism, such as estrogens, a component playing an important role as risk factor in BC development, especially in postmenopausal women. On the other hand, breast and gut resident microbiota are the link in the reciprocal interactions between cancer cells and their local environment, since microbiota are capable of modulating mucosal and systemic immune responses. Several in vivo and in vitro studies show remarkable evidence that diet, probiotics and prebiotics could exert important anticarcinogenic effects in BC. Moreover, gut microbiota have an important role in the metabolism of chemotherapeutic drugs and in the activity of immunogenic chemotherapies since they are a potential dominant mediator in the response to cancer therapy. Then, the microbiome impact in BC is multi-factorial, and the gut and breast tissue bacteria population could be important in regulating the local immune system, in tumor formation and progression and in therapy response and/or resistance.
Background
The analysis of gene expression levels is used in many clinical studies to know how patients evolve or to find new genetic biomarkers that could help in clinical decision making. However, the techniques and software available for these analyses are not intended for physicians, but for geneticists. However, enabling physicians to make initial discoveries on these data would benefit in the clinical assay development.
Results
Melanoma is a highly immunogenic tumor. Therefore, in recent years physicians have incorporated immune system altering drugs into their therapeutic arsenal against this disease, revolutionizing the treatment of patients with an advanced stage of the cancer. This has led us to explore and deepen our knowledge of the immunology surrounding melanoma, in order to optimize the approach. Within this project we have developed a database for collecting relevant clinical information for melanoma patients, including the storage of patient gene expression levels obtained from the NanoString platform (several samples are taken from each patient). The Immune Profiling Panel is used in this case. This database is being exploited through the analysis of the different expression profiles of the patients. This analysis is being done with Python, and a parallel version of the algorithms is available with Apache Spark to provide scalability as needed.
Conclusions
VIGLA-M, the visual analysis tool for gene expression levels in melanoma patients is available at
http://khaos.uma.es/melanoma/
. The platform with real clinical data can be accessed with a demo user account,
physician
, using password
physician_test_7634
(if you encounter any problems, contact us at this email address: mailto: khaos@lcc.uma.es). The initial results of the analysis of gene expression levels using these tools are providing first insights into the patients’ evolution. These results are promising, but larger scale tests must be developed once new patients have been sequenced, to discover new genetic biomarkers.
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